-[Description of Random Situation](#description-of-random-situation)
-[ModelZoo Homepage](#modelzoo-homepage)
# [LSTM Description](#contents)
This example is for LSTM model training and evaluation.
This example is for LSTM model training and evaluation.
## Requirements
[Paper](https://www.aclweb.org/anthology/P11-1015/): Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, Christopher Potts. [Learning Word Vectors for Sentiment Analysis](https://www.aclweb.org/anthology/P11-1015/). Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 2011
# [Model Architecture](#contents)
LSTM contains embeding, encoder and decoder modules. Encoder module consists of LSTM layer. Decoder module consists of fully-connection layer.
# [Dataset](#contents)
- aclImdb_v1 for training evaluation.[Large Movie Review Dataset](http://ai.stanford.edu/~amaas/data/sentiment/)
- GloVe: Vector representations for words.[GloVe: Global Vectors for Word Representation](https://nlp.stanford.edu/projects/glove/)